Computer Sciences Commons^™

Moonshine: An Online Randomness Distiller For Zero-Involvement Authentication, Jack West, Kyuin Lee, Suman Banerjee, Younghyun Kim, George K. Thiruvathukal, Neil Klingensmith

Computer Science: Faculty Publications and Other Works

Context-based authentication is a method for transparently validating another device's legitimacy to join a network based on location. Devices can pair with one another by continuously harvesting environmental noise to generate a random key with no user involvement. However, there are gaps in our understanding of the theoretical limitations of environmental noise harvesting, making it difficult for researchers to build efficient algorithms for sampling environmental noise and distilling keys from that noise. This work explores the information-theoretic capacity of context-based authentication mechanisms to generate random bit strings from environmental noise sources with known properties. Using only mild assumptions about the …

High-Order Flexible Multirate Integrators For Multiphysics Applications, Rujeko Chinomona

Mathematics Theses and Dissertations

Traditionally, time integration methods within multiphysics simulations have been chosen to cater to the most restrictive dynamics, sometimes at a great computational cost. Multirate integrators accurately and efficiently solve systems of ordinary differential equations that exhibit different time scales using two or more time steps. In this thesis, we explore three classes of time integrators that can be classified as one-step multi-stage multirate methods for which the slow dynamics are evolved using a traditional one step scheme and the fast dynamics are solved through a sequence of modified initial value problems. Practically, the fast dynamics are subcycled using a small …

Real-Time Virtualization And Coordination For Edge Computing, Haoran Li

McKelvey School of Engineering Graduate Student Theses & Dissertations

Recent years have witnessed the emergence of edge computing as an enabling platform for time-sensitive services. However, existing edge computing platforms face a multitude of challenges in meeting the latency requirements of time-sensitive applications. (1) Traditional real-time virtualization platforms require offline configuration of the scheduling parameters of virtual machines (VMs) based on their worst-case workloads. However, this static approach results in pessimistic resource allocation when the workloads in the VMs change dynamically. (2) Edge computing operators must deliver consistent tail latency performance for time-sensitive applications deployed on different edge sites. Traditionally, significant effort is required to test, tune, and configure …

Data Driven Architectural Geometric And Photometric Modeling, Huayi Zeng

McKelvey School of Engineering Graduate Student Theses & Dissertations

This thesis presents novel algorithms of architectural modeling, a crucial computer vision task to understand architectures by parsing visual input such as image or sensor data into digital representations. Compelling modeling algorithms serve as the fundamental block for a wide range of applications, including augmented reality, digital mapping, virtual simulation. While numerous handcrafted approaches have been proposed, the problem remains challenging in various difficult cases, such as occlusions and imperfect input. This dissertation studies four novel data-driven methods to perform high quality modeling. I first propose two geometric architectural modeling algorithms to recover the geometric primitives from aerial and facade …

Towards Deploying Robust Machine Learning Systems, Liang Tong

McKelvey School of Engineering Graduate Student Theses & Dissertations

Machine learning (ML) has come to be widely used in a broad array of settings, including important security applications such as network intrusion, fraud, and malware detection, as well as other high-stakes settings, such as autonomous driving. A general approach is to extract a set of features, or numerical attributes, of entities in question, collect a training data set of labeled examples (for example, indicating which instances are malicious and which are benign), learn a model which labels previously unseen instances presented in terms of their extracted features, and then investigate alerts raised by instances predicted as malicious. Despite the …

A Collaborative Knowledge-Based Security Risk Assessments Solution Using Blockchains, Tara Thaer Salman

McKelvey School of Engineering Graduate Student Theses & Dissertations

Artificial intelligence and machine learning have recently gained wide adaptation in building intelligent yet simple and proactive security risk assessment solutions. Intrusion identification, malware detection, and threat intelligence are examples of security risk assessment applications that have been revolutionized with these breakthrough technologies. With the increased risk and severity of cyber-attacks and the distributed nature of modern threats and vulnerabilities, it becomes critical to pose a distributed intelligent assessment solution that evaluates security risks collaboratively. Blockchain, as a decade-old successful distributed ledger technology, has the potential to build such collaborative solutions. However, in order to be used for such solutions, …

Stochastic Goal Recognition Design, Christabel Wayllace

McKelvey School of Engineering Graduate Student Theses & Dissertations

Goal Recognition Design (GRD) is the problem of finding the least amount of environment modifications to force an acting agent to reveal its goal as early as possible. Figuring out an agent’s goal by observing its behavior is a problem studied in Psychology, Economics, and Artificial Intelligence, where it is known as goal recognition. Contrary to most common approaches where the focus is on finding faster algorithms to detect the goal, GRD takes an offline approach and focuses on environment design to facilitate goal recognition. This thesis investigates GRD problems when action outcomes are stochastic, which is the case of …

Assessment And Diagnosis Of Human Colorectal And Ovarian Cancer Using Optical Imaging And Computer-Aided Diagnosis, Yifeng Zeng

McKelvey School of Engineering Graduate Student Theses & Dissertations

Tissue optical scattering has recently emerged as an important diagnosis parameter associated with early tumor development and progression. To characterize the differences between benign and malignant colorectal tissues, we have created an automated optical scattering coefficient mapping algorithm using an optical coherence tomography (OCT) system. A novel feature called the angular spectrum index quantifies the scattering coefficient distribution. In addition to scattering, subsurface morphological changes are also associated with the development of colorectal cancer. We have observed a specific mucosa structure indicating normal human colorectal tissue, and have developed a real-time pattern recognition neural network to localize this specific structure …

Machine Learning Methods For Depression Detection Using Smri And Rs-Fmri Images, Marzieh Sadat Mousavian

LSU Doctoral Dissertations

Major Depression Disorder (MDD) is a common disease throughout the world that negatively influences people’s lives. Early diagnosis of MDD is beneficial, so detecting practical biomarkers would aid clinicians in the diagnosis of MDD. Having an automated method to find biomarkers for MDD is helpful even though it is difficult. The main aim of this research is to generate a method for detecting discriminative features for MDD diagnosis based on Magnetic Resonance Imaging (MRI) data.

In this research, representational similarity analysis provides a framework to compare distributed patterns and obtain the similarity/dissimilarity of brain regions. Regions are obtained by either …

Quantifying Feature Overlaps In Deep Neural Networks And Their Applications In Unsupervised Learning And Generative Adversarial Networks, Edward Collier

LSU Doctoral Dissertations

Deep neural network learn a wide range of features from the input data. These features take many different forms from, structural to textural, and can be very scale invariant. The complexity of these features also differs from layer to layer. Much like the human brain, this behavior in deep neural networks can also be used to cluster and separate classes. Applicability in deep neural networks is the quantitative measurement of the networks ability to differentiate between clusters in feature space. Applicability can measure the differentiation between clusters of sets of classes, single classes, or even within the same class. In …

Musical Gesture Through The Human Computer Interface: An Investigation Using Information Theory, Michael Vincent Blandino

LSU Doctoral Dissertations

This study applies information theory to investigate human ability to communicate using continuous control sensors with a particular focus on informing the design of digital musical instruments. There is an active practice of building and evaluating such instruments, for instance, in the New Interfaces for Musical Expression (NIME) conference community. The fidelity of the instruments can depend on the included sensors, and although much anecdotal evidence and craft experience informs the use of these sensors, relatively little is known about the ability of humans to control them accurately. This dissertation addresses this issue and related concerns, including continuous control performance …

Quantitative Intersectional Data (Quinta): A #Metoo Case Study, Alicia E. Boyd

College of Computing and Digital Media Dissertations

This research began as an investigation of the #metoo movement, with the initial impetus to illuminate the voices located on the margins, those who often go unheard or are never recognized. This work aimed to understand the intersectional aspects of how these hashtag variations of the hashtag #metoo (i.e. #metoomosque, #churchtoo, #metoodisable, #metooqueer, #metoochina, etc) reveal the inequities of the #metoo movement on Twitter. The proliferation of these hashtag variations has often been ignored by scholars, and therefore absorbed into the larger #metoo movement conversation on Twitter. Therefore, the term `hashtag derivative' was created to describe the variation on the …

Ieee Access Special Section Editorial: Software-Defined Networks For Energy Internet And Smart Grid Communication, Mubashir Husain Rehmani, Alan Davy, Brendan Jennings, Zeeshan Kaleem, Akhilesh S. Thyagaturu, Hassnaa Moustafa, Al-Sakib Khan Pathan

Publications

A new network paradigm of software-defined networks (SDNs) is being widely adapted to efficiently monitor and manage the communication networks with a global perspective. SDN has a key networking feature that separates control and data plane. Today, due to its inherent benefits, SDN has been widely applied to various networking domains, including data centers, 5G Access and Core network functions, wide area network (WAN), enterprise, optical networks, underwater sensor networks (UWSNs), energy Internet (EI), and smart grid (SG).

Automated Analysis Of Rfps Using Natural Language Processing (Nlp) For The Technology Domain, Sterling Beason, William Hinton, Yousri A. Salamah, Jordan Salsman

SMU Data Science Review

Much progress has been made in text analysis, specifically within the statistical domain of Term Frequency (TF) and Inverse Document Frequency (IDF). However, there is much room for improvement especially within the area of discovering Emerging Trends. Emerging Trend Detection Systems (ETDS) depend on ingesting a collection of textual data and TF/IDF to identify new or up-trending topics within the Corpus. However, the tremendous rate of change and the amount of digital information presents a challenge that makes it almost impossible for a human expert to spot emerging trends without relying on an automated ETD system. Since the U.S. Government …

The Social Market Economy As A Formula For Peace, Prosperity, And Sustainability, Almuth D. Merkel

Doctor of International Conflict Management Dissertations

The social market economy was developed in Germany during the interwar period amidst political and economic turmoil. With clear demarcation lines differentiating it from socialism and laissez-faire capitalism, the social market economy became a formula for peace and prosperity for post WWII Germany. Since then, the success of the social market economy has inspired many other countries to adopt its principles. Drawing on evidence from economic history and the history of economic thought, this thesis first reviews the evolution of the fundamental principles that form the foundation of social-market economic thought. Blending the micro-economic utility maximization framework with traditional growth …

Reinforcement Learning For Realistic Robotic Training: A Survey, Andres Jaramillo

Honors Scholar Theses

Reinforcement learning is a widely popular topic that has resulted in a plethora of

research papers and interest from academia and industry. When applied with robotics,

the field has showed some promising signs that robots can achieve levels of complex

cognitive abilities rivaling humans, but the goal of creating sapient robots is far from

a reality due to many challenges involved with training robots in a real world setting.

This paper will provide a survey regarding the keys towards realistic robotic training by

detailing the challenges and overviewing the reinforcement learning solutions involved

in getting a robot to think like …

Federated Learning For Secure Sensor Cloud, Viraaji Mothukuri

Master of Science in Software Engineering Theses

Intelligent sensing solutions bridge the gap between the physical world and the cyber world by digitizing the sensor data collected from sensor devices. Sensor cloud networks provide resources to physical and virtual sensing devices and enable uninterrupted intelligent solutions to end-users. Thanks to advancements in machine learning algorithms and big data, the automation of mundane tasks with artificial intelligence is becoming a more reliable smart option. However, existing approaches based on centralized Machine Learning (ML) on sensor cloud networks fail to ensure data privacy. Moreover, centralized ML works with the pre-requisite to have the entire training dataset from end-devices transferred …

Towards Open World Object Detection, K. J. Joseph, Salman Khan, Fahad Shahbaz Khan, Vineeth N. Balasubramanian

Computer Vision Faculty Publications

Humans have a natural instinct to identify unknown object instances in their environments. The intrinsic curiosity about these unknown instances aids in learning about them, when the corresponding knowledge is eventually available. This motivates us to propose a novel computer vision problem called: 'Open World Object Detection', where a model is tasked to: 1) identify objects that have not been introduced to it as 'unknown', without explicit supervision to do so, and 2) incrementally learn these identified unknown categories without forgetting previously learned classes, when the corresponding labels are progressively received. We formulate the problem, introduce a strong evaluation protocol …

Airbnb Price Prediction With Sentiment Classification, Peilu Liu

Master's Projects

Airbnb is an online platform that provides arrangements for short-term local home renting services. It is a challenging task for the house owner to price a rental home and attract customers. Customers also need to evaluate the price of the rental property based on the listing details. This paper demonstrates several existing Airbnb price prediction models using machine learning and external data to improve the prediction accuracy. It also discusses machine learning and neural network models that are commonly used for price prediction. The goal of this paper is to build a price prediction model using machine learning and sentiment …

2vt: Visions, Technologies, And Visions Of Technologies For Understanding Human Scale Spaces, Ville Paanen, Piia Markkanen, Jonas Oppenlaender, Haider Akmal, Lik Hang Lee, Ava Fatah Gen Schieck, John Dunham, Konstantinos Papangelis, Nicolas Lalone, Niels Van Berkel, Jorge Goncalves, Simo Hosio

Presentations and other scholarship

Spatial experience is an important subject in various fields, and in HCI it has been mostly investigated in the urban scale. Research on human scale spaces has focused mostly on the personal meaning or aesthetic and embodied experiences in the space. Further, spatial experience is increasingly topical in envisioning how to build and interact with technologies in our everyday lived environments, particularly in so-called smart cities. This workshop brings researchers and practitioners from diverse fields to collaboratively discover new ways to understand and capture human scale spatial experience and envision its implications to future technological and creative developments in our …

Analysis Of Students’ Multi-Representation Ability In Augmented Reality-Assisted Learning, Sri Jumini, Edy Cahyono, Muhamad Miftakhul Falah

Library Philosophy and Practice (e-journal)

Not all learning sources can directly and cheaply be presented, so augmented reality media is needed to be applied to students with various talents and intelligence. This study aims to analyze students’ multi-representation ability through the use of augmented reality media. The research method was carried out through pre-experiment with one group posttest only design. Test question items were given to see the students’ multi-representation ability. Data analysis was carried out through the percentage of the number of students achieving test scores of more than or equal to 80 on a scale of 100. The results showed that 88% (28 …

Benchmarking Clustering And Classification Tasks Using K-Means, Fuzzy C-Means And Feedforward Neural Networks Optimized By Pso, Adam Pickens, Adam Pickens

Honors College Theses

Clustering is a widely used unsupervised learning technique across data mining and machine learning applications and finds frequent use in diverse fields ranging from astronomy, medical imaging, search and optimization, geology, geophysics and sentiment analysis to name a few. It is therefore important to verify the effectiveness of the clustering algorithms in question and to make reasonably strong arguments for the acceptance of the end results generated by the validity indices that measure the compactness and separability of clusters. This work aims to explore the successes and limitations of popular clustering mechanisms such as K-Means and Fuzzy C-Means by comparing …

Learning Intermediate Representations For Question Answering Systems, Zakery T. Clarke

Computer Science ETDs

Question answering systems are models that can perform natural language processing (NLP) on a question, retrieve an answer from a datasource, and communicate it to a user. In question answering systems, it is important for the system to learn an underlying representation for a piece of text. There are many systems that have achieved incredible accuracy on question answering datasets such as the Stanford Question and Answer Dataset (SQuAD), but these systems often encode their knowledge in a manner that is impossible to verify. Many current models would benefit more from verifiability, than marginal accuracy improvements.

We propose a method …

Analyses And Creation Of Author Stylized Text, Keith Carlson

Dartmouth College Ph.D Dissertations

Written text is one of the major ways that humans communicate their thoughts. A single thought can be expressed through many different combinations of words, and the writer must choose which they will use. We call the idea which is communicated the content of the message, and the particular words chosen to express the content, the style. The same content expressed in a different style may tell something useful about the author of the text (e.g., the author's identity), may be easier to understand for different audiences, or may evoke different emotions in the reader.

In this work we explore …

Fine-Grained Sentiment Analysis For Customer Review, Bing Han, Meng Han, Jing (Selena) He

Master of Science in Computer Science Theses

Natural Language Processing (NLP) is one of the most attractive technologies in many applications in real-life. Sentiment analysis, which has devoted to know others' think or feel about an experience or an item and hence take an action, is one of the most developed area in both academia and industry. Among sentiment analysis, fine-grained aspect sentiment analysis attempts to analyze emotional attitude categorized into different aspects or features of an(a) experience/service/product. Although aspect level sentiment analysis could provide more useful information, the proposed models' performance were relative poor compared with document-level or sentence-level sentiment analysis due to the lack of …

Model For Quantifying The Quality Of Secure Service, Paul M. Simon, Scott R. Graham, Christopher Talbot, Micah J. Hayden

Faculty Publications

Although not common today, communications networks could adjust security postures based on changing mission security requirements, environmental conditions, or adversarial capability, through the coordinated use of multiple channels. This will require the ability to measure the security of communications networks in a meaningful way. To address this need, in this paper, we introduce the Quality of Secure Service (QoSS) model, a methodology to evaluate how well a system meets its security requirements. This construct enables a repeatable and quantifiable measure of security in a single- or multi-channel network under static configurations. In this approach, the quantification of security is based …

Standard Non-Uniform Noise Dataset, Andres Imperial, John M. Edwards

Browse all Datasets

Fixed Pattern Noise Non-Uniformity Correction through K-Means Clustering

Fixed pattern noise removal from imagery by software correction is a practical approach compared to a physical hardware correction because it allows for correction post-capture of the imagery. Fixed pattern noise presents a unique challenge for de-noising techniques as the noise does not present itself where large number statistics are effective. Traditional noise removal techniques such as blurring or despeckling produce poor correction results because of a lack of noise identification. Other correction methods developed for fixed pattern noise can often present another problem of misidentification of noise. This problem can result …

Heuristically Secure Threshold Lattice-Based Cryptography Schemes, James D. Dalton

Masters Theses, 2020-current

In public-key encryption, a long-term private key can be an easy target for hacking and deserves extra protection. One way to enhance its security is to share the long-term private key among multiple (say n) distributed servers; any threshold number (t, t ≤ n) of these servers are needed to collectively use the shared private key without reconstructing it. As a result, an attacker who has compromised less than t servers will still not be able to reconstruct the shared private key.

In this thesis, we studied threshold decryption schemes for lattice-based public-key en- cryption, which is one of the …

In The Loop - Spring 2021 (Full Issue)

In The Loop

No abstract provided.

Silicon Valley 2.0

In The Loop

The DePaul Innovation Development Lab is a collaborative ecosystem that joins business and academia in mutually beneficial, experimental enterprise. Students work in the techcentric think tank and consultancy that turns business problems into functional, testable software prototypes.